The present invention relates to a method for generating time independent images of moving objects where the image information is related to properties of the moving object represented by parametric values.
In many practical, clinical, and industrial applications, the evaluation of properties in relation to a specific element of a system and the quantification of the variation of such properties with time is required. Methods are known for tracking the moving vascular wall (e.g. myocardium) from cardiovascular images in such a way as to extract from the image the properties (velocity, brightness, etc.) evaluated in relation to the tissue.
One driving example is the analysis of the time-course of blood flow in the capillary bed of the myocardium, based on echo-contrast imaging. The health of the myocardium can be established, among other ways, from the ability of the blood to reach all muscular cells; if a myocardial region is not perfused, then mechanical failure may occur (e.g. during angina or myocardial infarction). Therefore, it has been considered important to evaluate the perfusion properties of the different tissue regions. The quantification of myocardial perfusion is made by introducing a contrast agent in a vein, which moves with the blood. Quantification of its presence in the myocardial tissue is equivalent to quantification of myocardial perfusion (Becher and Burns, 2000). The analysis is made utilizing the ability of ultrasound machines to detect the echo enhancement deriving from a contrast medium that perfuses the myocardium. Recent examples of quantitative analysis of segmental perfusion are reported in literature (Mor-Avi et al., 1993; Wei et al., 1998; Masugata et al., 2001).
Crucial for an adequate quantification of the contrast signal is the ability to follow the systolic and diastolic movement of the heart walls. With respect to an ultrasound probe, the heart shows not only inherent movement but also displacements due to respiration. Moreover, the physician performing the examination can move the probe itself during the acquisition of the data. For these reasons, by trying to register the signal of the heart wall utilizing a region of interest (ROI) placed at a fixed location, the ROI frequently falls on other structures (such as the left or the right ventricular cavities or outside the heart). For these reasons, only if the heart wall is continuously tracked it is possible to extract the signal originating from the tissue and not outside of it and so to extract quantitative parameters of regional perfusion.
Such an approach has a widespread application—not only in echocardiography ((e.g., perfusion study analysis, regional wall velocity analysis and quantification, and computation of segmental strain and strain rate (Heimdal et al., 1998; Voigt et al., 2000)) but also in industrial applications when the tracking of a moving material is necessary, and in applications of visual recognition by intelligent electronic devices.
According to a first-known method, the quantification of wall-related properties is performed simply by analyzing the properties within an ROI (sometimes just a few pixels within the image), selected well inside the myocardial tissue. It is then important to verify that the selected ROI remains inside the tissue in all the images of the sequence; otherwise, information that does not pertain to the tissue may be included and the analysis may be corrupted. To make sure that erroneous samples are not introduced into the dataset, the sequence has to be reviewed frame by frame; when the ROI falls outside the tissue, it must be moved manually on the wall. It is evident how such an approach is inherently extremely time-consuming (in most cases, for each ROI more than 100 frames must be reviewed, and a compete evaluation requires an analysis of up to 20 different ROIs). Sometimes this procedure can be performed automatically with methods that depend from the software available. In most cases, these methods are based on standard edge detection algorithms or on cross-correlation alignment methods (Pratt, 1991); however, these techniques do not guarantee the accuracy of the results which must still be verified manually because they incorporate no information about the structure and the geometry of the object that is recognized.
In another method, the brightness of an echographic image is evaluated along pixels that are aligned on an ideal segment crossing the moving object; for example, crossing the myocardium and having an origin and an end outside the myocardium. This is done for several successive images so that the variation with time of the brightness of each pixel along the ideal segment can be simultaneously represented for all times in a two-dimensional representation where one axis is the distance along the segment and the other axis is the time. The brightness of a point defined by a particular distance-time coordinate corresponds to the brightness of the pixel residing at the corresponding position in the original image.
This solution allows for easier automatic tracking of the movement and deformation of the tissue of the myocardium, such as its center and thickness, and to errors in the position of the relevant pixel or group of pixels.
This method also gives the possibility to calculate, after wall motion compensation, for each pixel a time evolution of the perfusion. To this extent, N transmural segments are used to define a tissue region, N digital images (M-mode-like) are achieved, and the brightness of the pixels or the group of pixels along the said region passing through the wall of the myocardium is represented in relation to the time at which the relative image has been taken.
By choosing an appropriate sequence or loop of echographic images to be recorded, it is thus possible to show the evolution of a physical process like the tissue dynamics during a heartbeat or, using a contrast agent, the perfusion process of the organ during contrast vein infusion.
Particularly relating to the example pertaining to the perfusion, a certain number of frames are extracted as representing a certain number of digital images from a echo-contrast recording during myocardial perfusion. The perfusion of a tissue region is evaluated by the time-evolution (growth) of brightness in a region of interest, like a single pixel or a group of pixels.
A so-called perfusion curve is then used to represent the variation of the brightness of the pixel or group of pixels with time. In order to obtain valuable and comparable numeric data, an appropriate parametric curve is fitted to the measured data to obtain corresponding perfusion parameters. Actually, the parameters of the best approximation with a standard curve are taken as the perfusion parameters. A standard function given in the literature for myocardial perfusion is the following exponential function y(t)=A(1−e−Bt), where the two parameters A and B are the perfusion parameters. These two parameters contain synthetic information of the regional perfusion properties; two different measures can be compared by simply comparing the corresponding parameters. A quantification of echographic loops can be performed by extracting objective parameters from a region that can be quantitatively compared with the same parameters obtained in another region, in another patient, or in the same patient at different times. This so-called perfusion analysis is done differently in different applications.
Echography produces images of the relevant region in which the data of the reflected beam is not the pixel brightness itself. It is a graphical representation of a physical property of the organ under observation, like the reflectivity of the tissue, or, when using a contrast agent, the density of contrast bubbles, or the Doppler effects on the ultrasound echoes which gives information about the blood or tissue speed.
It appears clearly for the expert in the art that although the state of the art and the problem on which the invention is based is disclosed with detail with reference to perfusion, the technique according to the prior art and thus also the technique according to the present invention may be applied without need of further inventive activity and only by means of the basic knowledge of the expert in the art for representing the tissue properties measured by means of other physical parameters of the ultrasound echoes reflected by the tissue.
The above-disclosed known methods can give information of tissue properties related to certain parameters only by comparing the parameters which are pure numerical data. Since the information achieved is related to only one segment, the method must be repeated for each segment. Furthermore, it is difficult to clearly recognize and remember to which region of the object, such as the myocardial wall, the calculated parameters come from; therefore, no direct and immediate comparison is possible.